Omni’s qualitative analysis guide supports staff in conducting transparent, systematic, and actionable qualitative research in complex, real-world settings. Our clients often require timely, credible findings grounded in participants’ lived experiences. To meet these demands, this guide outlines practical workflows rooted in two complementary paradigms: pragmatism and critical realism.
Together, these paradigms support our commitment to meaningful, context-aware findings that are both usable and methodologically sound.
This guide provides step-by-step instructions for conducting qualitative analysis, from data pre-processing to reporting. It includes techniques like word frequency analysis, sentiment analysis, thematic coding, content analysis, and topic modeling, all implemented with transparency and adaptability in mind.
Tool Flexibility: While R is our preferred tool for reproducibility and integration with quantitative methods, the guide also includes best-practice workflows for using Dedoose (for team-based coding) and AI tools like NotebookLM (for early exploratory work).
Method Selection Guidance: We offer practical decision points based on dataset size, analytic goals, and project context, ensuring methods match the scope and purpose of each evaluation.
Structured Workflows: Omni workflows help analysts navigate all stages of qualitative analysis—from clarifying analytic frameworks and coding approaches to integrating qualitative and quantitative data in mixed-methods designs.
Adaptability: Whether you're working with rich interview transcripts or brief open-ended survey responses, the guide provides adaptable tools that emphasize rigor, transparency, and context.
| Version | Description | Date released |
|---|---|---|
| -4.0 | Adding interactive features | 2025-08-14 |
| -5.0 | Second draft for review and rebrand | 2025-08-14 |
| -6.0 | Initial draft for review | 2025-03-26 |
At Omni, we conduct qualitative research in complex, real-world environments. Our clients and projects often require fast timelines, nuanced insights, and transparent methods that can stand up to external review. To meet these needs, we use systematic, reproducible qualitative analysis methods grounded in pragmatism and critical realism.
This approach ensures our findings are actionable and reflect the realities and lived experiences of participants, while acknowledging the influence of context, interpretation, and limitations in our data.
This guide was developed to support Omni staff in conducting methodologically sound, transparent, and reproducible qualitative analyses. It offers practical, step-by-step instructions and highlights best practices for text pre-processing, analysis, and reporting. Whether working with interview transcripts, focus groups, open-ended survey responses, or other text-based data, Omni teams can use this guide to produce findings that are both grounded in participants’ voices and useful for program improvement and decision-making.
You’ll find guidance on:
Text mining / Natural Language
Processing (NLP)
(e.g., word frequency, sentiment analysis, topic modeling)
Thematic and content
analysis
(e.g., dictionary-based coding, thematic coding frameworks)
Narrative analysis @qualBPT do we need?
(Optional: e.g., structural analysis, language style matching)
Visualizing and reporting
qualitative data
(e.g., word clouds, bar charts, heatmaps, joint displays for
mixed-methods)
These methods can be applied to text from: Word documents (.docx), PDFs (.pdf), Zoom transcripts (.docx or .txt), open-ended survey responses (.xlsx or .csv), and more.
In many applied settings, qualitative methods (and especially mixed-methods) are described in vague terms. Reports may mention “themes emerging” or “triangulating findings,” but rarely explain the actual process used to get there. This vagueness makes it hard to replicate or assess qualitative findings and limits their credibility.
Several factors explain the common lack of clarity in methodology:
Different disciplines (e.g., public health vs. education research) use different frameworks, leading to inconsistent approaches.
Quantitative research has standard tools (R, SPSS, Stata), but qualitative tools (NVivo, MAXQDA, Dedoose) often rely on manual processes and don’t always integrate cleanly with quantitative workflows.
Qualitative analysis requires human interpretation and reflexivity. Documenting every step is time-consuming, and under tight timelines, many organizations skip this critical process.
Qualitative and quantitative teams often work separately, without shared standards or mixed-methods integration.
[Hannah is just messing around here to see how an .mp4 reads into R and plays]